African American Female Trajectories of Offending From Youth to Young Adulthood

Abstract

Using data from the Family and Community Health Survey, the current study explores developmental pathways from age 11 to 24 of African American males and females. This study describes the number and type of trajectories of offending for male and female African Americans, as much research in the past on trajectories has focused on White and/or male samples. We also investigate predictors of offending for the females both between and within trajectory groups. Results indicated that females who experienced higher levels of racial discrimination and greater parental hostility were more likely to be in a late bloomer group, compared with the low-level group. In addition, higher levels of racial discrimination and deviant peer affiliation were predictive of more offending.

Keywords

life-course criminology group-based trajectories female offending

Introduction

Delinquency is more likely to occur during adolescence than any other point during the life course (Moffitt, 1993, 1997). Given that adult offending is predicted by experiences in childhood and adolescence, there is a need to determine early predictors of delinquency. There is also a lack of gendered research on trajectory groups, with the majority of studies focusing on male only samples, or with few females in the sample. Many scholars have found that males and females operate in different ways, from their emotional development (Barnes & Beaver, 2010; Hoffmann & Cerbone, 1999; Moffitt, 1997; Young, 2014), cognitive development (Donnellan, Ge, & Wenk, 2000; McCuish, Corrado, Lussier, & Hart, 2014; Moffitt & Silva, 1988), and through their friends and peer influencers (Akers & Sellers, 2009; Brook, Lee, Finch, Brown, & Brook, 2013; Dong & Krohn, 2016; Matsueda & Anderson, 1998). Given the research above, there is ample reason to believe that this gender difference in development may appear in trajectory groupings and delinquency predictors (Giordano, Cernkovich, & Rudolph, 2002; Heimer & De Coster, 1999). Furthermore, there have been several calls to researchers to explore these developmental patterns more fully (Evans, Simons, & Simons, 2016; McGee & Mazerolle, 2016; Powell, Perreira, & Mullan Harris, 2010).

Theoretical Framework

The life-course paradigm provides a useful framework to examine patterns of behavior over time. Moffitt (1993, 1997) proposed that childhood behaviors and experiences influence the path that an individual will follow. If one engages in deviant rather than conforming behavior, Moffitt argues that two different deviant courses could be taken. She identifies these two major pathways as adolescent-limited and life-course-persistent trajectories. Moffitt (1993, 1997) also investigated different factors that may predict membership in one of these trajectory groups. Common risk factors include poor parenting, cognitive disorders, and other psychological characteristics. The antisocial behaviors that stem from these risk factors can contribute to negative outcomes across the life course and to those who will persist over time (Moffitt, 1993). Those who will be grouped into a life-course-persistent trajectory are individuals who continue to engage in delinquency beyond adolescence and into adulthood with a steady or increasing level of antisocial behavior (Moffitt, 1997). In contrast, those who follow an adolescent-limited trajectory engage in antisocial behaviors during adolescence and essentially “outgrow” or “phase out” from delinquent behaviors and/or status offenses as they enter adulthood (Moffitt, 1997). Adolescent-limited offenders and life-course-persistent offenders may be exposed to different amounts of risk factors and therefore the ability for adolescent-limited offenders to exit this difficult developmental stage is hampered by fewer barriers as compared with life-course-persistent offenders.

Moffitt’s work was foundational in trajectory research; however, more recent work has built upon those findings and highlighted important new considerations. One of the most influential findings has been the presence of more than two groups of offenders. Most past research on trajectory groups finds three to five distinct groups of offenders (Evans et al., 2016; Piquero & White, 2003), with many of the groupings being classified in similar ways across studies. These groupings often include a group of “non-offenders,” with little antisocial behavior shown during childhood and through adolescence (Roisman, Monahan, Campbell, Steinberg, & Cauffman, 2010); “low rate offenders,” a childhood antisocial behavioral group who desist in adolescence (Park, Lee, Bolland, Vazsonyi, & Sun, 2008); a group of “late onset” adolescence offenders (Farrington, Ttofi, & Coid, 2009; Piquero & White, 2003); and a “high rate,” persistent offenders from childhood through adolescence (Farrington et al., 2009; Young, 2014).¹

Recent work has also highlighted and more fully examined the effect of predictors of and risk factors for trajectory group membership. Farrington et al. (2009) found that the high-rate persistent offender group had the most significantly associated risk factors compared with all other groups; however, other studies have found variation in risk factors being a solid predictor of continued offending, or persistent offending into adulthood (Bersani, Nieuwbeerta, & Laub, 2009; Tzoumakis, Lussier, Le Blanc, & Davies, 2012). Other common risk factors include cognitive abilities (Moffitt, 1993, 1997; Piquero & White, 2003; Wolfgang, Figlio, & Sellin, 1972), delinquent peers (Dong & Krohn, 2016; Warr, 2002), family structure factors (Besemer & Farrington, 2012; Pires & Jenkins, 2007; L. G. Simons, Su, & Simons, 2013; Yonai, Levine, & Glicksohn, 2015), victimization (Daigle, Cullen, & Wright, 2007; Kim & Lo, 2015; Lauritsen & Laub, 2007), race/ethnicity (Bradshaw, Schaeffer, Petras, & Ialongo, 2010; Evans et al., 2016; Park et al., 2008), and gender (Giordano et al., 2002; Krohn, Hall, & Lizotte, 2009; Moffitt, 1994; Silverthorn & Frick, 1999).

Trajectory Research on Race and Gender

Research on gender-based trajectories is lacking, even though most researchers assume that males and females may have different trajectory patterns and different risk factors that influence their offending over the life course (Landsheer & van Dijkum, 2005; Silverthorn & Frick, 1999). Outside of life-course research, there is substantial support for offending and criminality to be vastly different for males and females (Giordano et al., 2002; Heimer & De Coster, 1999). Moffitt (1994) argued that males would be more likely to be life-course-persistent offenders than females. Silverthorn and Frick (1999) used this argument by Moffitt (1994) but furthered the conversation to suggest that a delayed onset of delinquency would occur among females. They suggested that females in the late-onset group were mimicking the behaviors of the early onset male group but at an older age (Silverthorn & Frick, 1999). White and Piquero (2004) tested this and found mixed results regarding gender differences, but did find support for Silverthorn and Frick’s (1999) finding that females in the sample who showed late onset were very similar to the early onset males in the same sample.

When looking at family predictors, Krohn et al. (2009) found no direct or indirect effect on females, but did find that family transitions influenced delinquency for males in their sample indirectly through their interactions with peers. Males are found to be more consistently violent in their delinquency than females (Walters, 2012; Zheng & Cleveland, 2013). Zheng and Cleveland (2013) found that among their four trajectory groupings, males and females were similar in their patterns of desisting; however, males were more likely to be in the high-rate chronic trajectory group and were more likely to be violent throughout all of the groups as compared with females.

Although the majority of life-course research has focused on all or mostly male samples, this has begun to change in recent years. Cauffman, Monahan, and Thomas (2015) investigated trajectories of female offending and risk factors for group membership. They found that the patterns of female offending were similar to males but with lower absolute levels of offending. Their results showed five groups of female offenders: a “low-level” group (39.9%), a “moderate” group (21.9%), an “early desister” group (21.3%), a “late desister” group (10.1%), and a “persister” group (6.6%). They were most interested in exploring factors that predicted desistance from crime, and results showed that for females the most important factors were exposure to violence, mental health problems, and adversarial interpersonal relationships (Cauffman et al., 2015). A second study investigated pathways of offending across race and gender using an Australian sample of males and females (Broidy et al., 2015). These researchers found five offending groups: “adolescent onset,” “low offending, adolescent onset,” “moderate offending, adolescent onset,” “chronic offending, adult onset,” “low offending, and early onset,” and “chronic offending.” Their results indicated that Indigenous Australians were less likely to be in “low-rate” offender groups than non-Indigenous Australians, and membership in this group was most pronounced in the non-Indigenous females. Their results mirrored patterns in the United States for comparisons between African Americans and Whites (Broidy et al., 2015). These results are in line with the majority of studies on trajectory groups, but added to this body of literature by investigating interaction effects of race and gender.

Finally, a recent study by Ahonen, Jennings, Loeber, and Farrington (2016) identified three trajectory groups of female offenders, focusing on serious property and violent offending. They identified non-offender, low-rate offender, and high-rate offender groups. Females who were in the “high rate” and “low rate” group were significantly more likely to experience an officially recorded police charge compared with those in the non-offender group. Their results also indicated that the high-rate offending females were more versatile in their offenses than those offending at lower rates (Ahonen et al., 2016).

Most of the prior research on trajectories has also focused on homogeneous samples, with Caucasian samples being heavily used. There is some research on African American samples that investigates life-course pathways and risk factors for delinquency and adult offending; however, the majority of these studies that have all African American samples are composed of only urban inner-city populations (Park et al., 2008; Park, Lee, Sun, Vazsonyi, & Bolland, 2010). Piquero and White (2003) examined the cognitive development of an urban African American sample and found that there were more individuals who fit into a life-course-persistent grouping compared with an adolescent-limited group, but a limitation of this research was the lack of diversity in the sample and the subjective criteria that were used to group the individuals.

The importance of studying samples of ethnic minorities is especially important when considering predictors of offending across the life course because racial discrimination has many implications for negative outcomes, including depression (Dubois, Burk-Braxton, Swenson, Tevendale, & Hardesty, 2002; R. L. Simons, Murry, et al., 2002) and victimization (Cullen, Unnever, Hartman, Turner, & Agnew, 2008). In addition, the cultural differences in parenting styles between racial and ethnic groups can determine a difference in manifestations of antisocial behavior (R. L. Simons, Wu, Lin, Gordon, & Conger, 2000).

Park et al. (2008) and Park et al. (2010) examined trajectory groups of an inner-city African American sample. They were able to identify three trajectory groups and found risk factors of substance use, self-esteem, and parental monitoring to be important influences on membership in specific trajectory groups. It is important to note that this sample by Park et al. (2008) and Park et al. (2010) was made up of inner-city African Americans but included both genders. Also using a mixed-gender sample, Bradshaw et al. (2010) sought to use experience in early childhood to predict negative outcomes later in the life course. They found among their sample of inner-city African Americans from Baltimore that “early starters” were more at risk for negative outcomes later in life (both males and females), as compared with the “later starter” trajectory groups.

There has been a variety of research comparing groups of Hispanic youth from two different geographical locations (Jennings, Maldonado-Molina, Piquero, & Canino, 2010; Maldonado-Molina, Piquero, Jennings, Bird, & Canino, 2009). Both of these studies compared a group of youth (males and females) from the Bronx, NY, with youth from San Juan, Puerto Rico. They found substantively similar results across the youth from both samples of Hispanic youth and across males and females. The authors found that males were slightly more frequent in their offending and had higher risk factors for delinquency, compared with females, and similarly, that youth from the Bronx were slightly higher on the same factors compared with youth from San Juan. Powell et al. (2010) also looked at trajectories of youth from Hispanic (and Asian) backgrounds. They found that first-generation Asian females and second-generation Hispanic females have heightened risk of delinquency in early adolescence; however, the delinquency rates fall across all their populations as the youth age.

In a prior study using the same data as the current study, Evans et al. (2016) examined trajectories of offending among African American males. They found results consistent with that of Park et al. (2008) and Park et al. (2010), with multiple trajectory groups discovered and the patterns of early risk factors increasing the levels of later participation in higher level trajectory groups. Evans et al. (2016) specifically highlighted the negative consequences of racial discrimination and in line with Burt, Simons, and Gibbons’s (2012) findings that discrimination and crime are related. In addition, they found that chronic offending group members were more likely to experience racial discrimination compared with the low-level group members (Evans et al., 2016).

The Current Study

It is crucial to continue to explore trajectory groups and expand our knowledge of developmental pathways among diverse samples of individuals. By continuing this research, we can further discover important risk factors and explanations for behavior patterns from childhood to adolescence and into early adulthood. Although the frameworks provided by Moffitt’s (1994) taxonomy work have provided important information on the development of delinquency, there remain unanswered questions regarding patterns of behavior over the life course. As Jennings and Reingle (2012) suggest, not only categorizing but being able to find predictors for group membership is crucial to help children through adolescence by identifying preventive techniques. There has been a clear lack of gendered research on trajectory groups and further a lack of African American samples studied that are not restricted to an only inner-city sample; however, there has been a call from trajectory researchers to explore the understudied populations of females, racial groups, and diverse social classes (Evans et al., 2016; Jennings et al., 2010; Powell et al., 2010). One of our goals is to explore the number and shape of offending trajectory groups for African American females, but even if the number and/or nature of these groups is the same for males and females, it may be the case that the proportion of females in each group is different than what has been found for males. It is also possible that predictors of behavior within and/or between groups may differ by gender.

The current study will address both of these needs by adding to the body of research on offending trajectories in both of these ways. Using the Family and Community Health Survey (FACHS), an all African American sample drawn from rural and suburban areas we seek to uncover female developmental patterns and factors influencing behavior within trajectory group. Although some past research has investigated trajectories of female offending, to our knowledge none have used an African American sample and none have explored predictors of offending between and within groups.

We expand upon the research outlined above on female patterns of offending by estimating trajectories of offending up to age 24 for males and females. Second, we investigate early predictors and time-varying risk factors for female delinquency throughout adolescence and early adulthood. First, we will determine the number of distinct groups of offenders as well as their patterns of offending. Next, we will examine the extent to which risk factors for offending identified in previous research (e.g., racial discrimination and deviant peers) influence the offending patterns for individuals within each female group.

Methods

The data used for the current study are FACHS. This dataset comprises African American residents of suburban Iowa and Georgia. This dataset is currently in its seventh wave of data collection and contains a large amount of information regarding the growth and development of participants from age 11 to 25 and information from their primary and secondary caregivers, sibling, and romantic partners. Respondents in Georgia were located in several different areas within the state, whereas in Iowa, they were drawn from one of two geographic areas. In both locations, community members were hired to serve as liaisons between the larger community members and research staff (R. L. Simons, Simons, Burt, Brody, & Cutrona, 2005). Wave 1 was collected in 1997 and included 867 African American, fifth-grade children. The subsequent waves were collected every 2 to 3years, with Wave 6 data collection in 2011. At Wave 6, almost 80% of the original sample participated in data collection. To be included in the current study, respondents must have valid responses to the criminal offending measure (described below) for at least three out of the five waves of data examined. The sample size varied slightly across models, but our core sample of females and males is in line with other studies using the same waves of this data. Prior work has also found little evidence for attrition other than at random (Burt, Lei, & Simons, 2017). The samples from Iowa and Georgia did not differ significantly in demographic characteristics. For more detailed sampling information, see R. L. Simons, Lin, et al. (2002)

Measures

Delinquency and crime measures

We used two different measures in the dataset to create variables representing delinquency and criminal offending. The original measure included 26 items that make up the conduct disorder scale from the Diagnostic Interview Schedule for Children, Version 4 (Disk-IV; described in more detail below). These items include a variety of antisocial acts, ranging from status offenses to serious crimes. The items for status offenses are detailed in the description of the “Delinquency” measure below. Given that this study follows individuals from early adolescence into adulthood, we could not include the status offenses in this measure for the purpose of constructing trajectory groups, as all items are not consistent once the adolescents reach the age of 18. Once these respondents reach the age of 18, the status offenses are no longer included as antisocial behaviors because they are now too old to be detained and/or punished for engaging in these behaviors. In light of this, we chose to include items that were consistent across all waves and use early delinquency as a predictor of trajectory group membership in the second stage of analysis.

Self-reported offending

The dependent variable for the trajectory group models is a self-report measure of 11 illegal behaviors varying in severity and type. Questions ask respondents to report how often in the past year they committed each offense (with the exception of the first wave, which asks “have you ever?”). Responses to each item were dichotomized into a variable indicating “0” if they had not engaged in the behavior and “1” if they reported the behavior at least one time. Behaviors include shoplifting, vandalism, assault, and marijuana use, among others. In Waves 1 to 4, these items were taken from the Disk-IV (American Psychiatric Association, 1994), with the remaining two waves using the measure created by Huizinga and Elliott (1986). Although the wording of the questions varies slightly between the two measures, the overall items are comparable. We use measures from Waves 2 to 6 to construct the trajectory models. Coefficient alpha values for each wave were as follows: Wave 1: .62; Wave 2: .55; Wave 3: .62; Wave 4: .40; Wave 5: .81; and Wave 6: .80. This measure has been used in a number of past studies to capture antisocial behavior (Burt et al., 2012; Evans et al., 2016; R. L. Simons & Burt, 2011; Su, Simons, & Simons, 2011).

Delinquency

The first independent variable is a control measure of delinquency at about age 11, taken from Wave 1 target reports. These items are also taken from the larger conduct disorder measure on the Disk-IV (American Psychiatric Association, 1994). Respondents were asked to report how often in the past year they engaged in these five behaviors: got in trouble for staying out too late, lied to get money or something else, skipped school/work, run away from home, and bullied or threatened others. Early antisocial behavior is a well-supported predictor of more serious crime and delinquency (Farrington, 2000; Virkkunen, Goldman, Nielsen, & Linnoila, 1995; Webster-Stratton & Hammond, 1997). Coefficient alpha for Wave 1 was .57. Although the alpha value is less than desirable for this measure, it has good face validity and has been used (along with others) to measure delinquency in the past.

Risk factors

Parental hostility

Parental hostility is measured by 13 items that ask the respondent to self-report on their parents’ behavior toward them. Questions ask “how often in the past 12 months did your mom . . .” engage in behaviors such as “get so mad at you that she broke or threw things,” “threaten to hurt you physically,” “push, grab, hit, or shove you?” Responses included “never,” “often,” “sometimes,” and “always.” The measure included is from Wave 1; coefficient alpha was .72. This measure is consistent with similar ones used in prior research (Evans et al., 2016; L. G. Simons, Chen, Simons, Brody, & Cutrona, 2006).

Class

The measure of class is composed from the primary caregivers’ report of their occupation and household income (Billingsley, 1992). It is a categorical measure with five categories: 1 = nonworking poor; 2 = working-class poor; 3 = working-class non-poor; 4 = middle class; and 5 = upper class. The measure used in the current study reflects the most recent value available for each given respondent. After Wave 4, this measure is not included because most targets had moved out of their parents’ households and so those measures of class were no longer relevant.

Deviant peers

Deviant peers is measured by 19 items that ask the respondent “during the past 12 months, how many of your close friends have . . .,” with questions ranging from “purposely damaged or destroyed property that did not belong to them,” “stolen something worth $25 or more,” “hit someone with the idea of hurting them,” “used illegal drugs like marijuana, hashish, LSD, cocaine, downers, or crack?” Response categories were “none of them,” “some of them,” or “all of them.” Items range from deviant behaviors to criminal ones. The same questions are asked at Waves 1 to 6. Coefficient alpha values for each wave were as follows: Wave 1: .84; Wave 2: .87; Wave 3: .87; Wave 4: .79; Wave 5: .81; and Wave 6: .80. This measure has been validated in prior research with this dataset and has resulted in similar reliability coefficients as in this study (Evans et al., 2016; L. G. Simons, Sutton, Shannon, Berg, & Gibbons, 2017).

Experiences of discrimination

This item is measured by 13 self-report items that ask the respondent how often they have experienced a series of encounters. These include “someone said something insulting to you just because you are African American,” “a store owner, sales clerk, or person working at a place of business treated you in a disrespectful way just because you are African American,” “has someone suspected you of doing something wrong just because you are African American,” “has someone threatened to harm you physically just because you are African American?” Response categories included “never,” “once or twice,” “a few times,” and “several times.” The same questions are asked at Waves 1 to 6. Coefficient alpha values for each wave were as follows: Wave 1: .84; Wave 2: .90; Wave 3: .90; Wave 4: .90; Wave 5: .90; and Wave 6: .90. This measure has been validated in prior research with this dataset and has resulted in similar reliability coefficients as in this study (Burt et al., 2012; Evans et al., 2016).

Analytic Method

We employ a Group-Based Trajectory Model (GBTM) approach to estimate the relationships described below. This model allows for the objective identification of subgroups based on observed patterns of behavior over time. GBTM has been used in a number of disciplines and has many benefits for the study of criminal or delinquent behavior (Piquero, 2008). Nagin (1999, 2005; Nagin & Land, 1993) developed this methodology and refined the use of it in studying criminal and delinquent behavior. This model provides for the statistical identification of subgroups of individuals while also allowing the researcher to estimate the effect of both time-invariant and time-variant predictors of behavior (Nagin, 2005). Consistent with prior research on crime and delinquency using trajectory models (Ahonen et al., 2016) and the skewness of the outcome variable, we estimated a zero-inflated Poisson trajectory form. Following the identification of the most accurate number of groups, we estimated models with various parametric identifications that arrive at the overall best-fitting models as described below. We predicted trajectory group models for males and females separately.

The next group of analyses focuses on explaining offending behavior both within and between groups in the female sample. We estimated two models—the first measures the influence of several Wave I variables and their ability to predict trajectory group membership. These variables were taken from the wave of data collection prior to the start of the trajectories, as outlined by Nagin (2005). The second estimates the change over time in experiences of racial discrimination and deviant friends and how these affect behavior within each trajectory group. Nagin (2005) argues that when investigating the development of behavior over time, change tends to be incremental rather than extreme and in light of this it makes sense to estimate change within groups. The results can be interpreted the same as a typical regression model, but coefficient estimates refer effects within the subset of individuals in each trajectory group. Given that the focus of this study is on females, we estimate these two models for the female sample only.

Results

Descriptive statistics are included in Table 1. When looking across each gender group, it is evident that offending levels were low on average (less than one offense per person in each wave). This does not display offending within each group separately but rather the full sample. Levels of racial discrimination, parental hostility, and deviant peers were all fairly consistent across each wave, with a notable dip in the level of friends’ deviance at Wave 3. The average socioeconomic class level was at 3.5 (measured on a 0-5 scale).

Table 1.

Descriptive Statistics.

	Females (N = 287)				Males (N = 338)
	M	SD	Minimum	Maximum	M	SD	Minimum	Maximum
Criminal offending (trajectories)
W2 offending	0.24	0.63	0	4	0.27	0.76	0	6
W3 offending	0.25	0.64	0	5	0.26	0.8	0	6
W4 offending	0.16	0.46	0	3	0.13	0.48	0	3
W5 offending	0.58	1.33	0	10	0.9	1.76	0	10
W6 offending	0.32	0.9	0	7	0.58	1.44	0	10
Early predictors
Class	3.5	1.24	1	5	—	—	—	—
W1 discrimination	21.64	6.67	13	46	—	—	—	—
W1 deviant peers	22.44	4.21	19	43	—	—	—	—
W1 parental hostility	21.78	4.87	14	43	—	—	—	—
W1 delinquency	0.76	1.53	0	11	—	—	—	—
Time-varying covariates
W2 discrimination	21.35	7.21	13	47	—	—	—	—
W3 discrimination	22.02	7.22	13	47	—	—	—	—
W4 discrimination	22.74	7.02	13	46	—	—	—	—
W5 discrimination	20.58	6.63	13	47	—	—	—	—
W2 deviant peers	22.89	4.38	19	46	—	—	—	—
W3 deviant peers	11.29	4.14	5	24	—	—	—	—
W4 deviant peers	17.49	3.79	13	39	—	—	—	—
W5 deviant peers	19.25	5	13	37	—	—	—	—

Note. W: Wave.

Figure 1 shows the best-fitting model for female offending. We chose the three-group model based on a number of factors: the Bayesian Information Criterion (BIC), the average predicted probability (avePP) of group membership, and evaluation by the researcher on whether or not each additional group added substantive new patterns or information (Nagin, 2005). For the three-group model (see Figure 1), the BIC was the smallest compared with other estimated models with four and five groups, in addition to the avePP in each group being above .80 and no group having less than 5% of the sample included. Respondents were only included in the trajectory group models if they had valid responses for the criminal offending measure for at least four of the five waves of data included. More detailed information on model selection is available upon request.

Figure 1.

Trajectories of offending, females (N = 421).

The three-group model shows three distinct patterns of offending among females. The “low-level” group displays very little offending throughout adolescence and early adulthood and comprises 73.5% of the sample. Individuals in the “early starter” group engage in more offending in early adolescence, but decrease as they enter early adulthood. This group accounts for 9.3% of the sample. The final group, “late bloomer,” displays an unexpected pattern of offending. Individuals in this group begin with a low level of offending (less than one offense on average) in early adolescence. However, as these females enter early adulthood, they begin to show a marked increase in offending. This group comprises 17.2% of the sample. Although the relative differences may be important, even the highest rate offenders report less than two criminal offenses in the past year at Wave 6.

The best-fitting model for the male sample included four groups (Figure 2). We used the same criteria and process to identify this model as described above in reference to the female sample. More information regarding model selection is available upon request. Notably, the male sample had an overall higher level of offending as compared with the female sample (ranging from 0 to 4 rather than 0 to 2.5). Similar to the female sample, there was a “low-level” offending group that engaged in very little to no criminal offending across the study period, representing 70.7% of the sample. There was also an “early starter” group that displayed relatively high level of offending around age 13 but decreased through adolescence and into adulthood. There were two different types of “late bloomer” groups. One, labeled “late bloomers,” was very similar to the female group, engaging in little to no offending as an adolescent but increasing sharply when entering adulthood, followed by another decrease around age 24. This group makes up about 14% of the sample. The final group, “late bloomer rising,” was unique to the male sample. This group of males showed little to no offending as adolescents but increases as they enter adulthood, around age 19, and are continuing to increase their offending in their mid-20s. This group only comprised about 7% of the sample.

Figure 2.

Trajectories of offending, males (N = 338).

Next, we estimated the effect of several risk factors that predict trajectory group membership. Nagin (2005) recommends including these risk factors in the same model estimation procedure as the group identification using the “traj” add-on in Stata to avoid classification errors. The results from this analysis are included in Table 2. The low-level offending group is the base outcome group for this analysis, so each comparison represents the likelihood belonging to either the late bloomer or the early starter relative to the low-level group. Those individuals who reported greater parental hostility in Wave I (around age 11) were less likely to be in the early starter group as compared with the low-level group. The same was true for individuals who reported more delinquency at Wave 1. Females who reported higher levels of racial discrimination and greater parental hostility at Wave 1 were more likely to be in the late bloomer group compared with the low-level group. Finally, those who were in a higher socioeconomic class were more likely to be in the late bloomer group compared with the low-level group.

Table 2.

Predictors of Trajectory Group Membership in the Three-Group Female Model (N = 287).

	Coefficient estimate (SE)
Early starter
Constant	−6.83 (3.26)*
Class	−0.001 (0.29)
Racial discrimination	0.082 (0.05)
Deviant friends	−0.092 (0.11)
Parental hostility	0.177 (0.07)**
Delinquency	0.44 (0.20)**
Late bloomer
Constant	−7.36 (1.69)**
Class	0.45 (0.19)**
Racial discrimination	0.067 (0.03)*
Deviant friends	0.020 (0.05)
Parental hostility	0.091 (0.05)*
Delinquency	0.20 (0.16)

Note. Low level is reference group.

p < .05. **p < .01. ***p < .001.

The final stage of analysis involved predicting change over time within each group of female respondents. This model supports the inclusion of not only predictors prior to the start of the trajectories (outlined in the previous section) but also the estimation of the effect of time-varying covariates within each trajectory group. Results from this analysis are included in Table 3. Each one had significant effects in at least one of the trajectory groups included. Higher levels of racial discrimination were significantly linked to greater offending within the early starter and the late bloomer group. Deviant peers significantly influenced offending in both the early starter and late bloomer groups, suggesting that spending time with peers who engage in more deviant behavior has repercussions on these females’ offending behavior over time. Wald tests were performed to estimate the difference in these effects and none were statistically significant.

Table 3.

Time-Varying Covariates Influencing Level of Offending Within Trajectory Group (N = 287).

	Shift in trajectory per unit change in time-varying covariate (SE)
Racial discrimination
Low level	−.102 (0.14)
Early starter	.053 (0.01)***
Late bloomer	.028 (0.01)*
Deviant friends
Low level	−.090 (0.15)
Early starter	.100 (0.015)***
Late bloomer	.040 (0.02)**

p < .05. **p < .01. ***p < .001.

Discussion

The current study provides further evidence for the importance of investigating female offending. There has been a recent increase in these investigations, and our results add to the small amount of existing literature on female offending through the life course. Like Ahonen et al. (2016), we find three groups of female offenders; however, ours do not include a non-offender group but rather a low-level offender group, an early starter group, and a late bloomer offender group. These groupings are similar to a few of the other studies of female offending trajectory groupings by Broidy et al. (2015) and Cauffman et al. (2015), but instead of five groupings, we have found three. The absolute values for offending are lower than some previous studies investigating trajectories of offending using self-report measures such as Cauffman and colleagues (2015). However, the aforementioned study used sample of serious offenders, whereas our sample included some serious offenders but is at its core a community sample rather than one targeting those arrested or previously charged with an offense. Two studies by Park and colleagues (Park et al., 2008; Park et al., 2010) had samples more similar to the one used here and found fairly similar results in terms of magnitude of offending.

Unlike other studies investigating similar questions, our late bloomer group displays an interesting pattern of behavior. We find the emergence of a group of female offenders that increase their offending as they enter adulthood, but appear to be quickly “aging out” of crime as they enter their mid-20s, similar to Moffitt (1993, 1997) adolescent-limited group. In contrast, the male sample included an additional group of individuals that began to offend as they entered adulthood but continued that rise in offending. The male sample also had a higher absolute level of offending, as supported by many other studies (Krohn et al., 2009; Walters, 2012; Zheng & Cleveland, 2013). Our findings did not corroborate the presence of an “adolescent limited” and “life course persistent” group as predicted by Moffitt (1993, 1997). Most recent research investigating trajectory groups has found a more diverse set of groups than only these two, and these results add to the growing body of evidence that patterns of behavior are more complex than early conceptualizations may have indicated.

Second, we identify several important early predictors and time-varying factors that influence offending levels for females within each of these groups. Females with more hostile parents and higher levels of delinquency at Wave 1 were more likely to be in the early starter group rather than the low-level group. Females who were in a higher socioeconomic class, experienced more racial discrimination, and reported more hostile parents at Wave 1 were more likely to be in the late bloomer group compared with the low-level group. Key findings regarding effects within groups included the following: for the early starter group, peers with greater levels of deviance and higher levels of racial discrimination predicted more offending. For the late bloomer group, higher levels of racial discrimination and peers with greater levels of deviance were predictive of more offending. There were no significant predictors of offending within the low-level offending group. Implications of these findings, as well as limitations and conclusions are discussed below.

The findings regarding the number of groups and patterns of offending within groups are not surprising given prior research, with one exception. Past investigations into trajectories of offending have similarly found the presence of a low-level offender group and an adolescent-limited offender group, both with male and female samples (Ahonen et al., 2016; Park et al., 2008; Park et al., 2010). The presence of these two groups would be in line with Moffitt’s (1993, 1997) dual taxonomy theory. In contrast, we know of only a few studies (Fergusson & Horwood, 2002; Francis, Harris, Wallace, Soothill, & Knight, 2013) that have found the presence of an offender group where individuals actually increase their offending as they enter adulthood, as indicated by our late bloomer rising group in the male sample. Although they display a decreasing pattern of offending during adolescence, as they begin to enter adulthood (a time when most individuals are “aging out” of crime), this group’s offending rises. A separate study using the male sample from this dataset also found the presence of a group of offenders who increased offending as they entered adulthood (Evans et al., 2016), but only used data up to approximately age 19. Our results confirm that this group continued their increase in offending through their mid-20s. Although Moffitt’s (1993, 1997) original conceptualization of trajectory groups only included two main groups, research such as ours and the studies discussed above have highlighted the need to broaden our understanding of how many unique patterns of offending exist among both males and females.

In both gender samples, we found a more “classic” late bloomer group—the peak in offending for these individuals appears to happen around age 19 (later than the traditional age–crime curve would predict), but like that traditional curve these individuals decrease offending as they enter adulthood. For these individuals, as they enter adulthood, many are moving out of their parents’ homes and are no longer subject to that same level of control, which may result in a subsequent increase in offending behaviors. There is some evidence in line with this that supports the presence of “late bloomers” who offend at higher levels once they leave the supportive (and often more controlling) environment of their parents’ households (Krohn, Gibson, & Thornberry, 2013). Results from this study are supportive of these findings, but also bring about new questions regarding early female offending. Results are mixed as to whether or not parents exert different amounts of control over their male and female children (Endendijk, Groeneveld, Bakermans-Kranenburg, & Mesman, 2016). Furthermore, our finding that females in higher socioeconomic classes were more likely to be in the late bloomer group as compared with the low-level group may be supportive of this as well. Perhaps those females who come from more affluent homes are more tightly controlled and less able to offend as adolescents but when they leave their family of origin these opportunities increase and, for some, so does their offending. We cannot estimate the effect of later parental behaviors in this sample using these variables as the parenting measures are not included on the survey past Wave 4; however, our findings regarding parental hostility indicate that in early adolescence harsh forms of parenting increases the likelihood of following a path of increased delinquency, as described below.

There were a number of significant early predictors of trajectory group membership. Delinquency around age 11 was only predictive of one contrast. Females who reported higher levels of delinquency at Wave 1 were more likely to be in the early starter group compared with the low-level group. This is not surprising given the patterns of offending displayed by these two trajectory groups and is generally in line with other research that indicates early measures of offending are strong predictors of future behavior (Baldry, Winkel, Traverso, & Bagnoli, 2001; Bersani et al., 2009; Tzoumakis et al., 2012).

Parenting behavior has consistently been shown to be an important predictor of delinquency and crime, and our results support this as well. Females who experience greater parental hostility were more likely to be in the early starter and late bloomer group compared with the low-level group. Like other studies, we confirm that greater offending is associated with harsh treatment from parents (Amato, 2005; Darling, 2011; Farrington, 1989; Gershoff, 2002; Krohn et al., 2009; Zigler, Taussig, & Black, 1992). Our results provide novel information regarding this, however, given that we find significant effects for both group contrasts. In our sample, greater offending was associated with hostility but that was true regardless of whether it happened early in adolescence (as with early starters) or much later adolescence/early adulthood (as with late bloomers). These results provide further support for how important early parenting behaviors can be and the lasting influence they can potentially have, at least for females. This is also in line with past research detailing the effects of parenting on antisocial behavior. This finding is in contrast, however, to the Evans et al. (2016) study using the males in this sample, in which parenting practices (measured in the form of authoritative parenting) did not predict group membership. It is not surprising that parenting may matter in unique ways for females, based on existing research regarding gender differences in parenting practices (Blackwell & Piquero, 2005; Griffin, Botvin, Scheier, Diaz, & Miller, 2000; Lynskey, Winfree, Esbensen, & Clason, 2000).

Experience of racial discrimination was an important predictor both of trajectory group membership and also within trajectory group behavior. Consistent with prior research, experience with racial discrimination was an important predictor of offending among this population, but not as powerful as has been indicated by prior research with the males in this sample (Evans et al., 2016). Females who experienced more racial discrimination around age 11 were more likely to be in the late bloomer group compared with the low-level group (see Table 2). Racial discrimination emerged also as a predictor of offending within the early starter and late bloomer groups of females (see Table 3). These findings indicate two different phenomena related to discrimination: first, it can influence the longitudinal path of delinquency, but second, the experience of racial discrimination can also affect level of delinquency even within a group of similar adolescents. Those who experienced higher levels of racial discrimination also reported higher levels of offending within each of these trajectory groups. These findings are important given our focus on females in particular. There is evidence that African American males experience greater racial discrimination on average compared with African American females (Dubois et al., 2002; R. L. Simons, Murry, et al., 2002; Stewart, Baumer, Brunson, & Simons, 2009; Williams & Mohammed, 2009), and furthermore, most research investigating negative outcomes of discrimination have been conducted with male samples (Burt et al., 2012; Steffensmeier, Ulmer, & Kramer, 1998). Past research has found that African American females are not subjected to the same degree of negative stereotyping as African American males with regard to criminal offending (Steffensmeier et al., 1998; Welch, 2007). Our results indicate that even if females experience less discrimination, when they do this discrimination can still influence offending in important ways.

Affiliation with deviant peers was also a powerful predictor of offending within the early starter and late bloomer groups. Females who reported greater deviance among their friends also had higher levels of offending. The effect of peer deviance on one’s own offending is not surprising in light of the large amount of past research on this topic studies (Button et al., 2007; Miller, Loeber, & Hipwell, 2009; Osgood, Wilson, O’Malley, Bachman, & Johnston, 1996). Our findings provide further evidence of the power of peer delinquency among a sample of African American females. It is important to note that similar to prior deviant peer literature, the deviant peer measure as a time-variant variable significantly influenced the level of delinquency within trajectory group (Nagin, 2005; Simons-Morton & Chen, 2006). However, when measured at wave 1 as a time-invariant variable, it was not associated with a greater likelihood of belonging to the late bloomer or early starter group as compared with the low-level offending group. These results indicate even among similarly behaving adolescents (i.e., within trajectory group), peer behavior can still influence offending levels over time.

Implications for Treatment and Programming

If this study is replicated in subsequent research, it would help to progress forward our knowledge about predicators of female offending with diverse youth populations. Although it may be hard to justify addressing policy to each individual issue that affects female offending patterns, it may be useful to create an integrated approach that is cost-effective and targets a range of factors. Based on the current study, an intervention effort might focus on improving parenting by decreasing hostility and harshness, increasing prosocial peers, and providing coping skills for dealing with racial discrimination.

This intervention goal may be reached through a variety of programming opportunities that target parental training (see Dishion & Andrews, 1995), prosocial mentoring, and resilience training for racially discriminated against youth. According to Office of Juvenile Justice and Delinquency Prevention (OJJDP; 2018), there are many kinds of programs available for youth that are supported as being effective. For racial discrimination, adopting a promising program like ERASE-Stress (Enhancing Resilience Among Students Experiencing Stress) may be beneficial to learn coping mechanisms, reduce post-traumatic stress disorder symptoms, decrease anxiety and functional programs, and increase social support (OJJDP, 2018).

These treatment programs that are directed on early problems that youth may feel that are predictive of later offending behaviors. A focus on peer and family factors is important based on our findings to prevent entry into deviant peer groups or problematic family practices. For example, in the current study, harshness of parenting leads to membership in both groups with higher offending levels. Tucker, Widmer, Faddis, Randolph, and Gass (2016) found that families who are involved in family therapy and positive engagement have better long-term outcomes, compared with families who do not participate in family therapy and engagement treatment. This suggests that having family focused treatment programming may be useful to train parents about their parenting techniques to reduce harshness and hostility with children.

Given the results of the current study suggesting an increase in offending during a transitionary stage from youth to young adulthood, programs like the Better Futures Program partially focus on transitions for juveniles and self-determination (OJJDP, 2018). This type of program may help females to adjust to leaving home and provide the opportunities to desist from offending through career training, hope, and prosocial support. Patterson, Chamberlain, and Reid (1982) have shown how parenting training can reduce offending in youth, so having programs that help with both ends of the life course may be helpful to affect both parenting practices while the child lives at home, and then as they transition out of their home and onto their own.

Given some of the contrasting results from the same sample with males (see Evans et al., 2016), we posit that the males and females do operate in unique ways. Due to the finding that there are gender differences, treatment and programs that target gendered approaches may be beneficial to reduce delinquency with a targeted child. Perhaps a program that target issues directly related to girls would be beneficial for addressing some of the uniqueness found in the current samples spike when the girls reached 19. For instance, Hubbard and Pratt (2002) suggest in their meta-analysis about delinquency and girls that emotionality of females may be an underlying issue with parenting issues, delinquent peer acceptance, and school issues. Using a program that targets the emotional and cognitive differences in males and females may help to address some of the predictors found in the current study, especially in relation to emotionally distressing incidents like racial discrimination.

Finally, our finding regarding affiliation with deviant peers suggests that increasing the social competence of a child would be beneficial to preclude deviant peer associations. A preventive effort to increase social competence in adolescence as they mature, and help them develop prosocial skills, may provide the necessary tools to reduce the influence of deviant peers on female offending. Many studies have shown that peer rejection and affiliation with deviant peers are highly predicative of delinquency and later offending behavior for youth (King, McLaughlin, Silk, & Monahan, 2017; Parker & Asher, 1987; Thomas, 2016). A program like the Big Brothers Big Sisters (BBBS) Community-Based Mentoring (CBM) Program, which is supported by OJJDP (2018) as being effective under their model program guide, may be a good option to increase positive relationships with both parents and peers. In addition, self-worth is a targeted outcome of the program, which may help to increase resilience to racial discrimination. In addition, a program like BBBS-CBM is based on the community which can address issues from the environment. As the current study participants are from a suburban area, a program set in that community may look slightly different than a similar program in a rural or urban environment.

Limitations

Although this study investigated an area with little existing research, there are limitations of the sample and method that should be considered when interpreting the results. First, even though the inclusion of only African American females was an intentional choice, it nevertheless limits the generalizability of the results. We cannot assume that these results are indicative of other racial/ethnic or gender groups. Furthermore, the sample used is drawn from largely suburban areas, so our results may not be indicative of a more urban population. Given that prior research tends to focus on urban populations, we believe this limitation helps to set apart our findings and add more nuanced information to the body of research on female offending.

Second, our measure of criminal offending did not use status offenses during adolescence, a necessary limitation to estimate trajectories ranging from adolescence to adulthood. Given that adolescent offending is more common when considering delinquency rather than criminality, the number and shape of the trajectories in this study are likely lower than what would be expected if including status offenses as well. We attempt to overcome this limitation somewhat by including delinquency as an independent variable in our study. Related to this point, the dependent variable represents a variety of offenses, and as a result we cannot make assessments as to how severity of offending may differ across groups. This is an important question that future research should consider. Although the independent variables we chose to include are all major predictors of antisocial behavior, this was certainly not an exhaustive list of these predictors. Finally, although we used the maximum data possible given the constraints of our models, there is the potential that we lost respondents between waves due to incarceration or other important dropout reasons, and the loss of these respondents may bias the findings. Our sample sizes were in line with prior research using this dataset (Burt et al., 2017). Future research should use mixed ethnicity and gender samples and additional predictors to further disentangle processes contributing to offending over the life course.

Conclusion

The results from the current study contribute to the growing but still limited body of research on life-course patterns of female offending. We add to this research by using an African American sample of female offenders from ages 11 to 25. Our results indicate that female patterns of offending are similar but not identical to those found in studies of males. Furthermore, we investigate family and individual predictors of offending within trajectory groups and find support for the effects of status offending, racial discrimination, parental hostility, and deviant peers on offending over time. Future work should continue to focus on female populations of offenders to more fully disentangle the processes contributing to offending over the life course.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Heart, Lung, Blood Institute (R01 HL118045), the National Institute on Child Health and Human Development (R01HD080749), the National Institute on Aging (R01 AG055393), the National Institute on Drug Abuse (R21DA034457), and the National Institute of Mental Health (R01 MH62699, R01 MH62666). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Notes

ORCID iD

Sara Z. Evans

Author Biographies

Sara Z. Evans is an assistant professor in the Department of Criminology & Criminal Justice at the University of West Florida. Her research interests include juvenile delinquency, the effect of family relationships on behavior problems in children, life-course and developmental criminology, and quantitative methodologies. Her work has focused on the effects of harsh discipline on both internalizing and externalizing behaviors in children.

Frances P. Abderhalden is a graduate student in the Department of Criminology and Criminal Justice at the University of West Florida. Her research interests include corrections, familial influence on criminality, and crime across the life course and social disorganization theories. Her current research focuses on siblings’ impact on the recidivism of career criminals.

Leslie G. Simons is a professor of Sociology at the University of Georgia. Her current research focuses on the socio-contextual predictors and consequences of various family processes, chief among them being parent-child relationships.

Ronald L. Simons is a distinguished research professor in the Department of Sociology at the University of Georgia, co-director of the Center for Gene-Social Environment Transactions, and senior fellow in the Owens Institute for Behavior Research. His current research investigates the manner in which social factors become biologically embedded and influence development and health across the life course.

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